Electrochemical Reactivity of Flavonoids and Flavonoid-Metal Ion Complexes with the Superoxide Anion Radical
Bibliographic record
Abstract
Reactive oxygen species (ROS), such as the superoxide anion radical, O2ׄ-, are by-products of aerobic cellular metabolism.1,2 ROS may serve as signaling molecules in some cellular processes, depending on the balance of ROS produced and ROS scavenging mechanisms at work.1 However, they may also lead to undesirable reactions in biological systems.1 Specifically, an excess production of ROS can cause oxidative stress, contributing to cellular death and the development of some pathological diseases, like cancer and Parkinson’s disease.1-3 Dietary flavonoids serve as antioxidant supplements to fight against ROS-induced damages in the system.4 Flavonoids are also known to play a role as metal ion chelators, in a biological environment, which subsequently modulates the antioxidant capacity of the flavonoid compound.1,4 Hence, greater understanding of flavonoid chemistry with ROS in the context of metal ions is needed. In this work, a previously established three-electrode electrochemical assay was used to evaluate the reactivities of flavonoids and flavonoid-metal ion complexes with electrochemically-generated superoxide anion radical, O2ׄ-.5 Cyclic voltammetry (CV) was used with a glassy carbon working electrode, a Pt wire counter electrode and Ag/AgNO3 reference electrode immersed in DMF, in this assay. CV was used to measure the electrochemical signal associated with O2ׄ- and its modulation in the presence of a specific flavonoid and metal ion (Cu(II), Zn(II), Fe(III), Mn(II), Cd(II)). Data indicated that flavonoids depleted a signal associated with O2ׄ- and that this reactivity was regulated in the presence of metal ions. References 1 P. Sharma, A. B. Jha, R. S. Dubey, and M. Pessarakli, J. Bot., 2012, 1–26 (2012). 2 M. P. Murphy, Biochem. J., 417, 1–13 (2008). 3 C. L. Bourvellec, D. Hauchard, A. Darchen, J.-L. Burgot, and M.-L. Abasq, Talanta, 75, 1098–1103 (2008). 4 K. E. Heim, A. R. Tagliaferro, and D. J. Bobilya, J. Nutr. Biochem., 13, 572–584 (2002). 5 N. L. Zabik, S. Anwar, I. Ziu, and S. Martic-Milne, Electrochim. Acta, 296, 174–180 (2019).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".